We’re entering the final stretch of another eventful year for data and machine learning professionals. Many of you are making one last push to learn new skills, catch up with recent research, or prepare for your next career move before the holiday season really kicks in in many parts of the world.
Our selection of must-reads from November covers a great deal of ground and echoes the topics and interests our community has focused on in recent weeks—from working with knowledge graphs to streamlining the job-search process. We hope you explore these excellent articles as you make plans for the new year just around the corner. Enjoy!
In case you missed them, here are some of our most-read and -shared posts from the past month.
How to Convert Any Text Into a Graph of Concepts
Learn how you can transform any text corpus into a knowledge graph using the Mistral 7B model: Rahul Nayak’s well-illustrated (and massively successful) guide covers the entire process in detail.Apple M2 Max GPU vs Nvidia V100, P100 and T4
Who doesn’t like a solid hardware benchmarking post? Fabrice Daniel took the time to compare Apple Silicon M2 Max GPU’s performance to Nvidia V100, P100, and T4 for training MLP, CNN, and LSTM models with TensorFlow.How I Got a Data Analyst Job in 6 Months
From the essential skills you’ll need to innovative ways to leverage generative AI, Natassha Selvaraj’s latest contribution helps job seekers become more efficient amid an increasingly competitive job market.Hidden Markov Models Explained with a Real Life Example and Python code
If you’re ready to roll up your sleeves and tinker away with some code, Carolina Bento’s accessible end-to-end guide on hidden Markov models is one that’s well worth your time.Photo by Christine on UnsplashThe New Best Python Package for Visualising Network Graphs
For his debut TDS post, Benjamin Lee walks us through the inner workings of gravis, an open-source package with powerful features for network-graph visualization.From Linear Algebra to Deep Learning in 7 Books (Winter 2023 Update)
Not sure how to spend your downtime in the coming weeks? This curated list of book recommendations by Andreas Stöffelbauer is a great resource, and covers a wide range of topics—statistics, neural networks, and more.Retrieval-Augmented Generation (RAG): From Theory to LangChain Implementation
Retrieval-augmented generation continues to make a splash in the ML community; if you’re just tuning in, Leonie Monigatti’s primer is a great place to start: it covers the fundamental principles powering this approach and also offers a detailed practical implementation.
Notable projects and conversation-starters
If you’re looking for inspiration or want to stay up-to-date on the lively discussions shaping the field, these picks are for you.
How Human Labor Enables Machine Learning
The concept of human-in-the-loop gets a new twist in Stephanie Kirmer’s recent article, which focuses on “how much manual, human work we rely upon to make the exciting advances in ML possible.”Build a Language Model on Your WhatsApp Chats
Some of us might think of our group chat as a fun space for meme- and gif-sharing. Bernhard Pfann, CFA took his as the basis for a GPT-based language model.Lost in DALL-E 3 Translation
How does the input language affect the outputs of a generative-AI text-to-image tool, and what can the results tell us about models’ built-in biases? Yennie Jun’s latest post explores this crucial topic in great detail.Do These 5 Simple Things to Make Your Data Scientist Resume Stand Out From the Crowd
From a focus on measurable impact to concrete formatting tips, Madison Hunter’s guide to successful resume-building is an essential read for anyone who’s thinking of switching roles in the foreseeable future.My Life Stats: I Tracked My Habits for a Year, and This Is What I Learned
It takes a lot of dedication to track one’s daily habits for a year, which might be why Pau Blasco i Roca’s debut article struck a chord with so many readers; even if you don’t plan to embark on a similar journey, it’s a compelling account of collecting and analyzing data.
Our latest cohort of new authors
Every month, we’re thrilled to see a fresh group of authors join TDS, each sharing their own unique voice, knowledge, and experience with our community. If you’re looking for new writers to explore and follow, just browse the work of our latest additions, including ming gao, Armand Sauzay, Mantek Singh, Zachary Raicik, Angela K., Flavien Berwick, Corné de Ruijt, Bhaskara Govinal Badiger, Ty Stephens, Nabil Alouani, Tim Rose, Shubham Agarwal, Mert Atli, Tom Gotsman, Vincent Vandenbussche, Onur Yuce Gun, PhD, Ahmed Fessi, Robert Constable, David R. Winer, Marcin Stasko, Luis Medina, Hugo Lu, Stijn Goossens, Samuel Chaineau, Jimmy Weaver, Summer He, Mathieu Laversin, Maksym Petyak, Sanil Khurana, Carlos J. Uribe, Chris Bruehl, Gurjinder Kaur, Sergei Savvov, Olivier Ruas, Andrew Skabar, PhD, Kaustubh Bhavsar, Pau Blasco i Roca, Victoria Walker, Fraser Brown, Victor Murcia, Juan Jose Munoz, Aveek Goswami, Matteo Ciprian, Benjamin Lee, Dima Timofeev, Petru van der Walt Félix, Frank Wittkampf, Paul Levchuk, Evgeniya Sukhodolskaya, Rodrigo Silva, Alex Shao, Jeremy Arancio, Liz Li, Michael Allen, Noah Haglund, and Amy Ma, among others.
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Until the next Variable,
Knowledge Graphs, Hardware Choices, Python Workflows, and Other November Must-Reads was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.